Search Results (41,849)

Despite their significance as forensic targets, alkyl nitrites, classified as illegal drugs, have received little attention in forensic analysis due to their high volatility and chemical instability. Here, we present a high-performance analytical approach using a pulsed dc soft plasma ionization-quadrupole mass spectrometry (pulsed dc SPI-MS) system, uniquely designed to operate using ambient air as the discharge gas. In this system, the modulation of the duty ratio functions as a “structural probe” to identify reactive isomers. Unlike conventional dielectric barrier discharge (DBD) sources that typically operate at atmospheric pressure, our SPI system utilizes a controlled pressure regime of several kPa, where the nitrogen in the ambient air effectively functions as a third-body gas to suppress excessive internal energy. The control of the duty ratio in our pulsed dc SPI source allowed for the successful manipulation of ion–molecule reaction pathways for highly reactive analytes. By optimizing several parameters, including duty ratio and discharge pressure, we achieved a unique ionization regime where the molecular-related ion [2 M − 3 H]⁺ was predominantly detected as the base peak with minimal fragmentation. Notably, by reducing the duty ratio from 50% to 5%, both the target ion occupancy and signal intensity were significantly enhanced, achieving a limit of detection (LOD) as low as 0.16 parts per million by volume (ppmv). This sensitivity is several orders of magnitude higher than previously reported thresholds, enabling rapid identification of C4–C6 alkyl nitrite isomers. This method transforms the duty ratio into a powerful diagnostic tool for identifying reactive intermediates, providing a practical and efficient approach for the onsite identification of illegal alkyl nitrites in forensic and security fields. Full article

Civic online participation platforms offer valuable opportunities to involve citizens in local governance and benefit from collective intelligence. Yet, vulnerable groups, such as older adults, people with disabilities, and the less educated, are often underrepresented in online political engagement. Aiming to empower these citizens to raise their voice online, we conducted two studies using an inclusive-by-design approach for developing an online civic engagement platform. In the first study, 39 individuals from two digitally low-performing European countries were surveyed about functions and features that would motivate and support their online participation. In the second study, focus groups with 13 digital and AI experts identified technical and informational requirements for effective use of the features desired by citizens. Our findings show the wishes for accessible, unbiased and secure AI-driven civic engagement platforms with transparency and user education about AI tools. In particular, chatbots require clear disclaimers and user guidance. Once citizens have been involved in the technical design process, both technical and informational feature preferences must be taken into account to avoid access and usability barriers or misunderstandings during the platform’s use. This is important to facilitate participation, especially for citizens from vulnerable groups. Full article

Building Information Modelling (BIM) offers a pivotal opportunity to automate Life Cycle Assessment (LCA) within the Architecture, Engineering, and Construction (AEC) industry. However, seamless integration is persistently hindered by a semantic gap, a critical misalignment between the object-oriented, geometric definitions of BIM and the process-based material data required by Life Cycle Inventory (LCI) databases. This paper presents a comprehensive review of data interoperability challenges and evaluates advanced methodologies designed to bridge this divide, moving beyond simple tool comparison to analyse structural integration barriers. Through a systematic review of 124 primary studies published between 2010 and 2025, this research inductively derives the BIM-LCA Interoperability Triad. This framework analyses causal dependencies across three dimensions, including Semantic and Ontological Structures, Workflow and Temporal Integration, and System Architecture and Interoperability. Furthermore, by establishing a comparative challenge–solution matrix, the analysis reveals a maturity paradox in current methodologies. While semi-automated commercial plugins dominate practice due to accessibility, they frequently function as opaque black boxes with limited transparency. Conversely, advanced approaches utilising Semantic Web technologies and Machine Learning demonstrate superior capability in resolving terminological mismatches but currently face significant barriers regarding infrastructure and expertise. This study contributes a novel theoretical model for understanding integration failures. It concludes that future research must pivot from static schema mapping towards AI-driven semantic healing, dynamic Digital Twins, and explicit system boundary harmonisation to achieve truly automated, context-aware environmental assessments and support whole-life circularity. Full article

Obesity is increasingly recognized as a disease of dysregulated intercellular communication rather than merely an energy imbalance. Extracellular vesicles (EVs), membrane-bound nanoparticles (30–1000 nm) released by nearly all cell types, act as central mediators of this pathological crosstalk. In obesity, hypertrophic adipocytes, pro-inflammatory macrophages, and dysfunctional endothelial cells secrete EVs carrying altered cargo, including pro-inflammatory miRNAs (e.g., miR-34a, miR-155), bioactive lipids, and stress proteins, which propagate systemic metabolic dysfunction. Adipose tissue-derived EVs impair hepatic fatty acid oxidation, promote steatohepatitis, suppress pancreatic beta-cell insulin secretion, induce skeletal muscle insulin resistance via PPARγ repression, and contribute to endothelial dysfunction and atherosclerosis. EV-mediated adipocyte–macrophage crosstalk reinforces chronic adipose inflammation. Circulating EVs also provide biomarkers: subpopulation ratios, miRNA signatures, and tissue factor-positive EVs reflect disease severity, predict cardiovascular risk, and monitor therapeutic responses, with machine learning enhancing diagnostic precision. Therapeutically, EVs from mesenchymal stem cells, Wharton’s jelly MSCs, adipose progenitors, and M2 macrophages reverse insulin resistance, hepatic steatosis, and adipose inflammation in preclinical models. Engineering strategies improve EV potency and tissue targeting, and Phase I trials confirm safety, though manufacturing and cost remain barriers. Preclinical and early clinical studies of MSC-EVs confirm a favorable safety profile, though manufacturing scalability and cost remain barriers to widespread clinical adoption. Overall, EVs represent both diagnostic tools and therapeutic vehicles in precision obesity medicine, offering a pathway from symptom management toward true disease remission. Full article

Background: Obesity is closely related to dysbiosis. Probiotics may improve metabolism and alleviate inflammation by regulating microbial–host interaction. Methods: Obesity was induced in rats by feeding a high-fat diet, followed by gavage administration of varying doses of BC99 as an intervention. Results: BC99 significantly reduced body weight gain, improved lipid profiles, alleviated systemic inflammation, and enhanced gut barrier integrity. 16S rRNA sequencing revealed that BC99 increased the abundance of beneficial bacteria, including Bacillota, Akkermansia, and Roseburia. Untargeted metabolomics showed that BC99 upregulated anti-inflammatory lysophosphatidylcholines (LysoPCs) and modulated conjugated bile acids (GUDCA, GDCA), which were correlated with enriched bile salt hydrolase (BSH)-active bacteria (e.g., Lachnoclostridium). Conclusions: The results indicate that W. coagulans BC99 effectively reduces weight gain in rats made obese by a high-fat diet and improves metabolic disorders. These effects are associated with remodeling of the gut microbiota and modulation of key metabolites, supporting a potential ‘microbiota–metabolite–host’ axis in rats that warrants further causal validation. Full article

The gastrointestinal mucus barrier (GIMB) is a gelatinous structure consisting primarily of mucins, water, and cathelicidin. Such a structure is the first line of defense against pathogens in the intestinal cavity and acts an important environment for the survival and reproduction of symbiotic flora. Mucin is mainly synthesized and secreted by intestinal goblet cells, forming a slime layer with different structures throughout the intestinal tract. The process of mucin synthesis and secretion is regulated by many factors, and there are some differences in the physical and chemical properties of the GIMB among animal species. Furthermore, recent studies have shown a close relationship among the mucus barrier, gastrointestinal diseases, and tumors. Soybean agglutinin (SBA) is a major glycoprotein in soybean that is closely related with the detection, prevention, and treatment of disease and cancer. Current studies indicate a close relationship between SBA and the GIMB, particularly at the molecular level and through species-specific differences in mucin glycan structures. Existing evidence shows that these differences affect the binding affinity and antinutritional effects of SBA. The novel relations between SBA and GIMB may become new targets for disease treatment. Full article

Skin and hair follicles regenerate through coordinated stem cell niches and cyclic signaling associated with transitions among anagen, catagen, and telogen phases. In alopecia and chronic skin diseases, follicular miniaturization, immune dysregulation, persistent inflammation, impaired vascularization, and a compromised stratum corneum barrier limit the effectiveness of conventional topical and systemic therapies. Bio-nanovesicles (BNVs), including natural extracellular vesicles such as exosomes and microvesicles, as well as engineered artificial or hybrid nanovesicles, offer a targeted, cell-free delivery platform for miRNAs, proteins, and growth factors. By modulating key pathways—Wnt/β-catenin, PI3K/AKT, MAPK/ERK, and TGF-β/BMP—BNVs have the potential to restore regenerative crosstalk, enhance angiogenesis, and help initiate hair and skin repair. Full article

Growing consumer awareness of environmental issues has led to heightened interest in environmentally responsible personal care products, with notable growth in the green beauty market. Despite this trend, consumer evaluations and purchasing decisions are shaped by numerous elements, particularly the trustworthiness of environmental information provided by brands. While elements such as eco-labels, environmentally friendly packaging, and individual concern for the environment are generally associated with favourable views of green marketing, they can also trigger doubt among consumers. This study investigates how eco-labeling, green packaging, and environmental concern influence consumer attitudes toward green beauty products and how these attitudes affect purchase intentions for them. The study included 500 South African consumer participants, and data were collected through an accredited research organization. The results indicate that eco-labeling, green packaging, and environmental concern each have a significant positive effect on consumer attitudes toward green beauty products. In addition, consumer attitudes were found to strongly influence purchase intentions among South African consumers. The study contributes to the literature by emphasizing the complex role of informational cues and potential perceptual barriers in sustainable consumption of green beauty products. It also provides practical insights for marketers, highlighting the importance of enhancing the credibility of eco-labels and reducing consumer doubt through clear, transparent communication strategies in the South African context. Full article

This study implements Active Flow Control (AFC) in the form of a dielectric barrier discharge (DBD) plasma actuator to enhance aerodynamic performance during heave–pitch motions on a three-dimensional NACA 0015 airfoil at a Reynolds number of $Re=5\times {10}^5$ using the Large Eddy Simulation (LES) turbulence method. The simulation at a reduced frequency of 0.14 incorporates two-degrees-of-freedom wing motion, allowing for simultaneous pitching and heaving motions with amplitudes of ${75}^{\circ }$ and a chord length ($1c$), respectively. We evaluate the impact of localized momentum injection via a phenomenological plasma actuator model across two force intensities. A low-force configuration (Case-LF) provides marginal control, whereas a high-force configuration (Case-HF) provides greater control than the baseline without plasma. After applying DBD plasma to the airfoil, flow-field analysis revealed that the plasma treatment significantly improved the lift coefficient. It showed that the lower plasma cases achieved a 1.46% improvement only on the $C{l}_{rms}$, a 14.57% reduction in the averaged $Cd$, and a 19.11% enhancement on the $C{l}_{rms}$-to-$C{d}_{avg}$ ratio. Furthermore, the cases with higher plasma forces resulted in significant improvements when compared to the Baseline and Case-LF; it showed a 11.65% improvement in $C{l}_{rms}$, 19.87% in $C{d}_{avg}$, and 39.8% in $C{l}_{rms}$-to-$C{d}_{avg}$ ratio when compared to the baseline. These results validate the effectiveness of plasma actuators in enhancing wing aerodynamic performance during such complex motions. Full article

Shooting activities offer educational and recreational value; however, their application in school physical education and recreational settings remains limited due to safety concerns, high costs, and restricted access to specialized facilities and equipment. To address these constraints, this study designed and implemented a low-cost laser shooting system suitable for school physical education and recreational use. The proposed system comprises a laser-gun module, a physical electronic target providing immediate on-site feedback using an illuminance sensor, a Fresnel lens, and RGB LEDs, and a web-based electronic target that enables real-time scoring, logging, and visualization via smartphone or tablet cameras and browser-based processing. By adopting a low-power, projectile-free laser structure with pulse-limited emission, the system enhances operational safety, while the use of general-purpose components and web standards reduces cost and lowers barriers to adoption. Technical verification conducted under controlled indoor conditions demonstrated stable single-shot operation, reliable hit detection, and accurate score calculation for both the physical and web-based targets. Expert validation involving specialists in physical education, educational technology, and sports technology yielded consistently high evaluations across safety, cost efficiency, functional completeness, and field applicability. These findings suggest that the proposed system represents a practical and scalable alternative for school physical education classes and recreational programs. Future research should examine user-level usability, learning outcomes, system robustness under diverse environmental conditions, and structured expert consensus processes. Full article

Recycled polypropylene reinforced with glass fiber (rPP-GF) represents a promising sustainable material alternative addressing global imperatives for environmentally responsible manufacturing. This systematic literature review examines the mechanical, thermal, and sustainability performance of rPP-GF composites based on 66 peer-reviewed studies published between 2014 and 2024, following PRISMA guidelines. The evidence demonstrates that glass fiber reinforcement significantly enhances mechanical and thermal performance. Compared to virgin counterparts, rPP-GF composites reduce environmental impact. However, technical challenges, including fiber attrition during reprocessing and a lack of standardized testing protocols, remain significant barriers. Future research should prioritize unified testing frameworks, hybrid filler systems, and advanced recycling technologies for enhanced circular economy applications. Full article

This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare. Full article

Background: Research concerning potential resolutions to immigrants’ health care access in Canada is limited, and the viewpoint of immigrant communities regarding priorities and feasible solutions remains inadequately captured. The objective of this article is to portray a research endeavor in which grassroots community members assumed the role of priority-setters for research on primary care access concerns. Aim: This cross-sectional study aimed to identify community-prioritized primary care access research topics among Nepalese Canadian immigrants in Calgary by ranking ten predefined issues based on perceived importance. Methods: We conducted community-based participatory research (CBPR) with the Nepalese community members in Canada. Participants were recruited using snowball sampling through community networks and rated topics using a 5-point Likert scale. A self-administered survey was used to collect participants’ rankings of ten predefined primary care access challenge themes. The themes were identified through comprehensive literature reviews undertaken by the research program team. The questionnaire was pilot-tested and refined based on feedback from team members before being administered. Results: A total of 401 Nepalese immigrants completed the survey, with 50.4% self-identifying as men. Among survey participants, significant gender differences were observed in sociodemographic characteristics, including age distribution, educational attainment, extended health insurance coverage, household income, and length of stay in Canada. Overall, health care cost and lack of resources were identified as the highest research priorities. While both men and women ranked these issues highly, women assigned greater priority to transportation- and culture-related barriers, whereas men generally assigned lower priority to these issues. Conclusions: There is a growing recognition that health solution priority-setting approaches should embrace transdisciplinary collaboration, with community participation as a pivotal factor. The results underscore the value of transdisciplinary, collaborative priority-setting approaches that center community participation to inform health research and interventions aligned with the needs of immigrant communities. Full article

Background: The Canada Health Act (CHA), enacted in 1984, guarantees universal access to medically necessary care, yet inequities in hospital use persist. Acute length of stay (ALOS) is a key indicator of hospital efficiency, patient recovery, and healthcare system performance, with prolonged stays linked to higher costs, avoidable infections, and strain on acute care capacity. Understanding patterns in ALOS is critical not only for hospital management but also for public health, as extended stays can limit timely access to care and exacerbate population-level health inequities. Objective: This study examines social, geographic, and clinical gradients in ALOS and investigates whether the effects of admission urgency vary by sex, neighbourhood income, and rural–urban residence within a universal healthcare system. Methods: Using 2024–2025 hospital discharge data from the Canadian Institute for Health Information, this study examined ALOS as a function of comorbidity, sex, socioeconomic status, rural–urban residence (geography), and admission type (urgent versus elective). Interaction effects between admission urgency and key social and geographic variables were evaluated to assess subgroup differences in ALOS. Results: Disparities in ALOS were evident. Older age, male sex, urgent admission, and greater comorbidity were associated with longer stays, whereas higher neighbourhood income and urban residence were linked to shorter stays. Interaction analyses revealed substantial heterogeneity: compared with elective rural admissions, urgent urban admissions had 30.4% longer ALOS. Urgent admissions also amplified socioeconomic and sex-based differences, with male patients experiencing 27.9% longer stays than females. Conclusions: From a public health perspective, these findings highlight how system capacity constraints and social inequities jointly shape hospital use. Reducing avoidable variation in ALOS will require policies that strengthen acute care surge capacity, improve coordination for urgent admissions, and address upstream socioeconomic and geographic barriers to care, thereby promoting more equitable and efficient hospital services. Full article

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems. Full article